System for the recognition of wear patterns on microstructures of carbon steels using a multilayer perceptrón

This paper describes the application of a recognition system wear patterns present in carbon steel, the system classifies the microstructure of the materials which have three conditions throughout life-time in thermoelectric plants. This approach employs the artificial neural network multilayer perc...

Descripción completa

Detalles Bibliográficos
Autor: Javier Yáñez Mendiola
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:México
Institución:Centro de Innovación Aplicada en Tecnologías Competitivas
Repositorio:Repositorio Institucional de CIATEC
Idioma:inglés
OAI Identifier:oai:ciatec.repositorioinstitucional.mx:1019/165
Acceso en línea:http://ciatec.repositorioinstitucional.mx/jspui/handle/1019/165
Access Level:acceso abierto
Palabra clave:info:eu-repo/classification/LEMD/Procesamiento de imágenes - Técnicas digitales
info:eu-repo/classification/LEMD/Inteligencia artificial
info:eu-repo/classification/cti/7
info:eu-repo/classification/cti/33
info:eu-repo/classification/cti/3304
info:eu-repo/classification/cti/330412
Descripción
Sumario:This paper describes the application of a recognition system wear patterns present in carbon steel, the system classifies the microstructure of the materials which have three conditions throughout life-time in thermoelectric plants. This approach employs the artificial neural network multilayer perceptron in conjunction with the digital image processing to recognize the different physical states of the materials used as conductors in conditions of high temperatures. The studied patterns in the microstructure are spheronization, decarburization and graphitization. The microstructure is revealed from microscope images obtained in the Testing Laboratory Equipment and Materials of the Federal Electricity Commission in Mexico (LAPEM-CFE). The proposed system compared to the human expert, obtained an accuracy of 96.83 % with a shorter analysis time and inspection cost.